Furthermore, a better experimental and theoretical understanding is needed of the basic settings of neural networks to support computation in time-frequency domain. For traditional real-value neural networks, researchers have good intuitions about the basic configurations of initialization, activation functions, dropout and normalization techniques, and optimization methods. However, for neural network in the time-frequency domain, our understanding is limited. Although the reseach community started to study the basic settings of neural networks with complex values [1]}, the current understanding remains preliminary. Time-frequency analysis can have operations in both the real and complex domains. At the same time, the underlying time-frequency information within the internal representations can make the related studies even more complicated. We believe that this understanding will greatly facilitate future design of deep learning systems for IoT.
